1.
The Art of Knowledge Engineering
or: knowledge engineering of art
Guus Schreiber

2.
Knowledge engineering:
From art to discipline
■ Objective of knowledge engineering (KE) in 90’s
■ Use of knowledge patterns for “expert” tasks

3.
The popularity of “ontology”
■ One of the first ontologies: traffic ontology
– “On representational promiscuity”
– “Vacuous paper with no content” (Brian Gaines, KAW 93)
■ Now seen as panacea for the Holy Grail of
information integration
■ There is even a Web language for it
■ But will it stand the test of time?
– are our current conceptions of formal classes and
properties sufficient to grasp the complexity of the Web?

4.
The Web: knowledge engineering
for the masses
■ KE is outside the former small research community
■ Everyone is building hierarchies and describes classes

5.
Intermezzo:
Knowledge democracy
■ From a human-rights point of view the Web is a leap
forward
■ Possibility for “everyone” to access knowledge
– the empowered citizen
■ But history will tell whether this remains true

6.
Web KE builds on a long tradition of
vocabulary construction

7.
Semantic annotation is now feasible

8.
Associative reasoning:
following Semantic Web links

9.
The popularity of
“ontology” alignment
■ Creating the (missing) links the the Linked Open Data
Cloud
■ Multitude of alignment techniques available
■ Large evaluation initiative
– OAEI
■ But will our alignment methods stand the test of
time? Are the results good enough?

10.
Semantic search types
■ DISAMBIGUATE: Can you give me alternative
interpretations of term T?
■ DESCRIBE: Can you give me more information about
concept C or Individual I?
■ QUESTION ANSWERING: does property P hold for
object O?
■ ANSWER QUESTIONING: Jeopardy!
■ RELATION SEARCH: in what way(s) are object O1 and
O2 related?

11.
Use cases for semantic search

12.
Picasso and Moulin de la Galette
Location-based:
relatively easy

13.
How are Picasso and Matisse
connected: Georges Braque
Style- and time-based
not trivial

14.
How are Picasso and Matisse
connected: 1907
the changes in the art world in Paris anno 1907
difficult

15.
Matisse and “les fauves”
Where does this term come from?
1905: the story of the critic
difficult

16.
The importance of narratives
■ Users like to get the “stories” behind the navigation
paths in the graph
■ For this we need to have some minimal
“understanding” of the meaning of the paths
■ Well-constructed minimal ontologies can provide
such interpretations
– graph patterns
■ Example: an event ontology

17.
Web mining of piracy events
from piracy reports & Web sources
van Hage, Malaisé & van Erp, 2011

18.
Problems in ontology alignment
■ We have not agreed on an adequate alignment
vocabulary
– misuse of owl:sameAs
■ We have no adequate methodology for evaluating
alignments (Tordai et al., 2011)
■ In particular, people do not agree on how different
classes align
– and this is not because they don’t do it “right”

19.
Beyond categories
■ The set theory on which ontology languages are built
is inadequate for modelling how people think about
categories (Lakoff)
– Category boundaries are not hard: cf. art styles
– People think of prototypes; some examples are very
prototypical, others less
■ We also need to make meta-distinctions explicit
– organizing class: “furniture”
– base-level class: “chair”
– domain-specific: “Windsor chair”

20.
KE for the Web: the way forward?!
■ We are only scratching the surface in semantic
search
– large-scale experimentation needed
– small minimal ontologies acting as search patterns
■ We need a revised alignment vocabulary
– taking Lakoff’s notions into account
■ Attention for semantic detail matters
– in search, in aligment, for story telling
– and lay knowledge engineers are providing it!
■ Combining this with statistical technqiues is a
powerful combination

21.
Are Picasso and Herengracht 196
related?